In recent years, there has been a method of presuming a type and an existence area of a target object by pattern matching an image of the target object which has preliminarily been learned and an image including a target object which was newly photographed (for example, refer to the Official Gazette of Japanese Patent Application Laid-Open No. 2002-32766). In the above method, an eigen value and an eigen vector are calculated from the image of the target object which has preliminarily been learned and registered. Such a device that the target object can be recognized by the smaller number of models by projecting an image cut out from the newly photographed image to its eigen vector has been made. A device to further raise a recognition rate by using partial learning images which are obtained by dividing the image of the target object which has preliminarily been learned and registered has also been disclosed.
However, according to the pattern matching method of the image represented by the foregoing method, there is such a problem that it takes a long time to recognize. Particularly, if the number of classes to be discriminated is very large, there is such a problem that a recognition time explodes. For example, it is now assumed that there are 1000 kinds of target objects (for example, faces of 1000 persons are registered) and there are 100 (also including a profile and the like) learning images (per object) photographed from various angles for each target object. Thus, 100000 learning patterns exist in total. Consequently, even if an image collation of one registration image and the new photograph image can be made for 10 nsec, one second is required to obtain a final recognition result.
As for the registration image mentioned here, for example, an inside of a circumscribed rectangle surrounding the target object is presumed. As for the new photograph image, an image of a large size including the registration image is presumed. Therefore, the image collation of one registration image and the new photograph image is not a simple image pattern matching of a one-to-one correspondence relationship but includes a position specifying task for specifying in which portion in the new photograph image the target object exists. Specifically speaking, for example, by cutting out the new photograph image by a sliding window and matching each cut-out image and the registration image, the image collation is realized. Therefore, although it depends on the number of pixels of each of the registration image and the new photograph image, the foregoing image collating time of 10 nsec can be regarded as a time obtained in the case where the images have been processed at a very high speed in terms of a current computer processing speed.
Further, in the related art, according to the image matching method using the partial learning images mentioned above, such a problem that the recognition time explodes appears typically. For example, now assuming that one learning image was divided into 100 (=10×10) overlap partial images, since the number of registration images to be matched is increased 100 times, a calculating time is also simply increased 100 times. In the case of the foregoing example, a time of 100 seconds is required.
Even if the number of dimensions was reduced by using the eigen vector by the foregoing method, although the calculating time of the distance calculation can be reduced, since a vector calculation adapted to reduce the number of dimensions cannot be omitted, a high speed of the total image recognition cannot be eventually realized. Such an essential problem that the image collation of the same number of times as the number of kinds of registration images is necessary is not solved at all.